A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes’ functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.


INTRODUCTION
Abnormal sleep duration is detrimental to cardiovascular health -increasing the risk of incident cardiovascular disease (CVD) and mortality -and inherently complex, with suspected heterogeneous effects according to sex and race/ethnicity (1,2).Deviation from healthy sleep can impact diurnal rhythms, hormone levels (e.g.ghrelin, cortisol), autonomous nervous system balance, and even remodel vascular structure -resulting in adverse consequences, such as reduced nocturnal blood pressure (BP) dipping and sustained daytime hypertension (1,3).
Yet the mechanistic pathways underlying the biomolecular connection between short and long sleep with cardiovascular health remain unclear.Evidence implicates heightened sympathetic tone and metabolic dysfunction in the mechanism of short sleep, but there remains a gap in clarity with the added complexity of interwoven pathways like oxidative stress and endothelial dysfunction (1,4).The role of long sleep is more elusive, with recent work highlighting the pertinence of in ammatory markers, underlying comorbidity burden (i.e.dyslipidemia, depression) and arterial stiffness metrics (5,6).This incomplete understanding of the intersection between habitual sleep duration and cardiovascular health necessitates further investigation.
Hypertension is a major risk factor for CVD, with blood pressure traits known to have a strong genetic background.Recent genome-wide association analyses (GWASs) have discovered more than 2,000 loci explaining ~ 40% of systolic or diastolic BP heritability among European descent individuals (7).It is important to investigate the role of sleep health in such a polygenic landscape.This may both explain additional heritability of BP traits, as well as bring to the forefront novel genomic loci that inform perspective on sleep's in uence on biomolecular pathways underlying BP.Moreover, incorporating diverse population groups is essential -as this can reveal novel gene targets speci c to particular subgroups or shared across -improving downstream therapeutic designs, and offering tangible insight to counter disparities in health.Our prior work in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Gene-Lifestyle Interactions Working Group highlighted novel non-overlapping gene-sleep interactions for BP, suggesting distinct roles of in uence for short and long sleep duration(8).Our current analysis advances the eld by including a 12-fold larger sample size and additional sex-strati ed analyses, yielding enhanced statistical power and granularity.

MATERIALS AND METHODS
This work was approved by the Institutional Review Board of Washington University in St. Louis and complies with all relevant ethical regulations.For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent.

Data Harmonization
Data from each cohort (Supplementary Tables S1-S2) were harmonized following this centralized protocol.Data were strati ed by population group, based on self-reported ancestry and individual cohort de nitions (AFR: African, EAS: East Asian, EUR: European, HIS: Hispanic/Latinos, SAS: South Asian), and sex (combined sex, female sex, male sex).Analyses considered 3 primary blood pressure (BP) traits as outcome variables (SBP: systolic, DBP: diastolic, PP: pulse pressure) and 2 dichotomous lifestyle exposures (LTST: long total sleep time, STST: short total sleep time).Genetic variants (G) were restricted to autosomal chromosomes 1-22 imputation quality≥0.3,and minor allele frequency≥0.1%.Age was restricted to ≥18 years, and reported total sleep time constrained within 3 and 14 hours.In scenarios of multiple visits, the single visit with largest sample size was utilized and in case-control study designs, cases and controls were required to be analyzed separately.For BP outcome measures, if multiple readings were taken in a single visit the mean was used.All BP values were winsorized at 6 standard deviations from the mean.BP values were adjusted for reported use of anti-hypertensive medications as follows: SBP (+ 15 mmHg) and DBP (+ 10 mmHg).PP was derived as SBP -DBP.In the case of studies with known between-sample relatedness, null model residuals (regressing BP traits on a kinship matrix/genetic covariance matrix) were denoted as the BP outcome.STST and LTST were derived from total sleep time (TST) by regressing TST on age, sex, age×sex and using the residuals' 20th and 80th percentiles as cutoffs (STST = 1 if ≤ 20th percentile, LTST = 1 if ≥ 80th percentile, STST = 0 if > 20th percentile, LTST = 0 if < 80th percentile).Covariates included population-group speci c principal components, cohort-speci c confounders (study center), age, age 2 , sex, age×S/LTST, age 2 ×S/LTST, and sex×S/LTST.Samples with missing data were excluded.
Summary statistics were centrally processed after individual studies submitted results.EasyQC2 software (www.genepi-regensburg.de/easyqc2)was used to perform quality control (QC) on resultant data (11).Data were ltered for degrees of freedom≥20 calculated as minor allele count * imputation quality (e.g.MACxR 2 provided by each cohort) within the unexposed, the exposed, and the total sample.Missing or invalid/out of range values for statistics and duplicated or monomorphic variants were discarded.hg19 genomic coordinates were lifted over to hg38 genomic coordinates.Allele frequency discrepancies relative to TOPMed-imputed 1000G reference panels (Trans-Omics for Precision Medicine imputed 1000Genomes) were assessed for each speci c population group, along with genomic control (GC) lambda in ation.Meta-level quality control was conducted within groups based on population group, with evaluation of unwanted centering of the outcome variable, outlying cohorts highlighting unstable numerical computation, or alarming in ation.

Meta-Analysis
Meta-analysis was designed as the following paradigm.Cross-population meta-analysis (CPMA) was designed to be combine all population group results, with additional focused population-group speci c and sex-speci c analyses.This resulted in 18 total meta-analyses to be run: 6 population groups (CPMA, EUR, HIS, EAS, AFR, SAS) and 3 sex groups (combined sex, female sex, male sex).To accomplish this, METAL software was rst used to run all metaanalyses within each speci c population group for the marginal effect (β M2_G ), main effect (β M1_G ), interaction effect (β M1_GxE ), and joint effect (β M1_G , β M1_GxE ) with GC correction for in ation (12).Inverse-variance weights were used and Manning et.al's method for the 2df joint test (13).CPMA was subsequently executed on the resultant population-group speci c METAL output results with GC correction.

Genome-wide Signi cant Loci Identi cation
EasyStrata2 software was used to prioritize top loci from signi cant results identi ed from the 1df interaction and 2df joint tests (14).GC correction for population-group speci c results was applied.Variants found within 1 Mb distance of the major histocompatibility complex (MHC) region were excluded.Either minimum sample size (N > 20000) or multiple cohorts (≥3) was required as necessary criteria for processing results from a speci c sex-strati ed, and/or population group-strati ed meta-analysis.
Signi cant variants were identi ed using the following threshold criteria: (i) i variants with signi cant interaction effect (P M1_GxE <5e-9, FDR < 0.05); (ii) j variants with signi cant joint effect (P M1_G,GxE <5e-9, FDR < 0.05) were ltered as top variants; and (iii) k top variants for the interaction effect were identi ed using a 2-step method -identifying rst z variants by the marginal effect (P M2_G <1e-5) and then ltering these by the interaction effect (P M1_GxE <0.05/N G, FDR GxE <0.05) where N G is the number of independent tests calculated using principal components analysis on the z variants.This 2-step method was incorporated to increase power for detecting interactions (15).This design was executed to maintain both stringent threshold criteria and incorporate false discovery correction implemented by the Benjamini-Hochberg method.
All such i + j + k signi cant variants were narrowed down to loci based on 500 kilobase (kb) regions.Finally, within these regions independent lead variants were identi ed as the top signi cant variant within the locus, subsequently de ning variants in LD as those with linkage disequilibrium (LD) r 2 threshold < 0.1 using TOPMed-imputed 1000G reference panels.If variants were missing in the LD panels, then the most signi cant variant within each 500kb region was retained for combined sex meta-analyses results.

Prioritizing Novel Sleep Duration Interaction Loci
Signi cant independent loci were subsequently ltered to prioritize gene-sleep duration interaction loci.From the 1df interaction test, X interaction loci were prioritized as those not found within 1Mb of previously identi ed gene-sleep duration loci for BP(8).Loci were annotated as whether novel for BP genetic architecture, or not, by checking for overlap with 1Mb of previous GWAS variants (Supplementary Table S3).
For the 2df test, rst loci were ltered to those variants not found within 1Mb of previous GWAS identi ed variants for BP traits, and with insigni cant marginal effect (P M2_G >5e-09, FDR M2_G >0.05).From these variants, Y loci were prioritized as driven by interaction if they harbored a stronger interaction effect relative to the main effect (P M1_GxE < P M1_G ), and Z loci deemed as supported (but not driven) by interaction if this was not true.Thus, collectively X + Y gene-sleep duration interaction loci were highlighted, alongside secondarily Z loci supported by interaction.

Heterogeneity by Sex
To test for interaction effects showing evidence of heterogeneity by sex (p < 0.05/Q), two-sample Z-tests assuming independence, were conducted for each of the top interaction loci and adjusted for multiple testing.

Variant Annotations
FAVOR was queried to annotate deleteriousness or functionality scores (17), and RegulomeDB v2.2 was used to extract aggregate regulatory function evidence scores, along with chromatin state, DNA accessibility, overlap with transcription factor (TF) binding sites or TF motifs, and expression quantitative trait loci (eQTL) (19).FUMA's SNP2GENE pipeline was used to annotate a comprehensive list of genes for each top locus, incorporating positional, chromatin interaction (FDR < = 1e-6, 250bp upstream − 500 bp downstream of TSS), and GTEXv8 eQTL evidence (agreeing with RegulomeDB) with the top variant or its variants in LD (r2 > 0.1 within 500kb)(18).

Discovery of Novel Gene-Sleep Duration Interactions
From an initial source of 37 studies, 59 population-group speci c cohorts (derived from self-reported ancestry) resulted in a pooled sample size of 811,405 individuals comprising of 5.9% AFR (12 cohorts), 6.0% EAS (5 cohorts), 83.4% EUR (34 cohorts), 3.7% HIS (7 cohorts), and 0.9% SAS (1 cohort) (Supplementary Tables S1-S2).The 1df test discovered seven loci and the 2-step method discovered one locus.The 2df joint test rst identi ed 3629 signi cant loci, from which 18 were novel for BP (Supplementary Table S3) with insigni cant marginal effect -revealing 14 loci driven by the interaction effect, and four not driven.Thus in total we discovered 22 gene-sleep duration interaction loci, and 4 secondary loci -of which 21 are novel for BP traits (Supplementary Table S4).Among the 22 prioritized interaction loci, four loci exhibited cross-population effects (Table 1, Supplementary Figures S1-S3)one identi ed in combined sex, three in female sex-strati ed analyses, and 18 identi ed speci c to either one of the AFR, HIS, or EUR population groups (Table 2, Supplementary Figures S1-S3).Speci cally, AFR analysis revealed one gene-sleep duration interaction locus, HIS analysis revealed 11 gene-sleep duration interaction loci, and EUR analysis revealed six gene-sleep duration loci (Table 2).Three variants identi ed in combined sex meta-analyses showed evidence of heterogeneous effect by sex (Table 2). 1 denotes the variant was identi ed using the two-step approach.
2 denotes variants novel for BP. 2 Empty cell in P sex_diff indicates the variant, after quality control, was not found in both sex-strati ed meta-analyses.All results herein are from the M1 model.Supplementary Tables S21-S22 provides summary statistics according to each population group-speci c cohort fo rs150586434, and according to each population group for rs34761985 and rs13032423 below.For the sex column, C denotes combined sex meta-analysis.E denotes effect allele, and O denotes other allele used as reference.Bold denotes signi cance.
1 denotes variants novel for BP.
2 Empty cell in P sex_diff indicates the variant, after quality control, was not found in both sex-strati ed meta-analyses.

Mapped Protein-Coding Genes
All 26 variants identi ed were either intronic or intergenic, and mapped to a primary set of 27 protein coding genes (Supplementary Table S7).Extended gene mapping revealed 292 genes highlighted for the 12 STST interaction loci, 67 genes for the 10 LTST interaction loci, and 35 genes for the four joint 2df loci not driven by interaction (Supplementary Tables S7-S9).

Gene Set Enrichment Analysis
We performed gene set enrichment analyses on the aforementioned extended gene sets in the FUMA GENE2FUNC platform and STRING database (Supplementary Tables S18-S19)(18, 24).STST-mapped genes highlighted pathways in antioxidant defense and neuron excitation, along with phenotypic connection to lipid levels, neurological health, cardiovascular health, metabolism and immune defense.LTST-mapped genes implicated traits involving in ammation, neurological health, and metabolism.A clearly distinctive pattern differentiating short and long sleep duration interaction loci was thus not observed.

DISCUSSION
In this large-scale effort investigating the biomolecular mechanisms underpinning the intersecting roles of sleep health and blood pressure traits, we conducted genome-wide gene-by-sleep duration (short and long sleep) interaction analyses in 811,405 individuals of diverse population backgrounds (AFR, EAS, EUR, HIS, SAS) for systolic blood pressure, diastolic blood pressure, and pulse pressure.We report novel discovery of 22 gene-sleep duration interaction loci for BP traits -12 for short sleep, and 10 for long sleep.Several of the identi ed variants are rare with allele frequency < = 1%, with four variants identi ed in sex-strati ed meta-analyses, and 18 variants speci c to either the AFR(1), EUR(6), or HIS (11) population groups.In line with our previous research, the identi ed genomic loci exhibiting interactions with short and long sleep are non-overlapping (with non-signi cance in the opposing sleep duration exposure), suggesting distinct mechanisms in uencing cardiovascular health.Nonetheless, we did not observe a clear differentiating pattern in the biological pathways implicated when comparing short sleep and long sleep.
The functional annotation investigations of our prioritized genes point towards cardiovascular and neurological connections, along with revealing links to circadian rhythm, thyroid function, bone health, and hematopoiesis mechanisms.Our ndings highlight potential pharmacological candidates and suggest pertinent pathways to consider when designing holistic therapeutic regimens for improving blood pressure control.
Firstly, at a broad level, several identi ed genes are tied to neurological mechanisms.KCNJ3 encodes Kir3.1 -the alpha subunit for the I KACh potassium channel -and is interestingly implicated in bradyarrhythmia by its missense variant inducing a gain of function of I KACh , as activation of this channel is tied to the negative chronotropic effect on heart rate exerted by the parasympathetic nervous system (29).CRBN is linked to cognitive function (30), SDK1 promotes synaptic connectivity (31), ZNF521 regulates neuron cell fate (32), and ATP8A2 is involved in both neuron vesicle transport and cardiac conduction (33).Further, KRTAP13-2, WWOX, EFNA5, and ALCAM are linked to nervous system development with additional roles for WWOX in myelination (34) and EFNA5 in vascular sympathetic innervation (35).These functional connections may suggest a potential nervous system-heart connection that could be in uenced by sleep or circadian disturbances.
In fact neurological pathway connections to circadian rhythm reveal themselves through two enzymes -PAK5 and PAM.Given that circadian rhythm and clock gene expression is intimately connected to blood pressure patterns, of note is PAK5 -a serine/threonine kinase protective of adult neurons from injury and ischemic stress(36).PAK5 has both been shown to be targeted by clock gene-regulated miRNAs in the liver and identi ed to strongly bind to 14-3-3 proteins -a protein family connected to light-sensitive melatonin diurnal patterns and plausibly in uential for sleep behavior (37,38).This strong binding a nity to 14-3-3 proteins suggests an interesting connection, as YWHAB (one of this study's primary genes mapped to a STST interaction locus), is part of this protein family.Another enzyme informing the neurological-sleep axis is PAM, encoding a copper-dependent enzyme important for synthesizing amidated neuropeptides like NPY -which regulates sleep through noradrenergic signals (39,40).
Further, TG and JMJD1C, both encoding proteins intrinsically tied to thyroid hormone function (thyroglobulin and thyroid receptor-interacting protein 8 respectively) -present suggestive ties to the intersection between thyroid function and circadian rhythms.TG mRNA and protein expression levels have shown to increase in response to melatonin, along with its genetic variants associated with autoimmune thyroid diseases (41,42).Gene silencing of JMJD1C's paralog has shown arrhythmicity and prolonged sleep in Drosophila (43).Given that circadian clock and thyroid function are increasingly suggested to be interconnected, and sleep deprivation can disrupt temporal hormone pro les (e.g.increased morning plasma thyroid-stimulating hormone (TSH) levels), it may be valuable to investigate further the overlapping pathways between thyroid function, healthy sleep duration, and cardiovascular morbidity (44).
Beyond thyroidal pathways, hematopoiesis presents a possible comprehensive perspective on the interconnectedness between sleep health and nervous system response.WBP1L, one of the primary genes identi ed (mapped to a STST interaction locus identi ed in female-speci c CPMA) has suggestive connection to regulating the CXCL12-CXCR4 signaling pathway by its inhibitory role on CXCR4, the receptor for ligand CXCL12 (45).This pathway is both in uential for in ammation and hematopoietic state, re ects circadian control, and directly implicates the sympathetic nervous system response -pertinent as stressors are suspected to induce a more exacerbated response in females (46,47).If stress factors (e.g.sleep loss) induce noradrenaline, this can downregulate CXCL12, with resultant increased cell proliferation of pro-in ammatory cells from the bone marrow, incurring vascular damage(46).For instance, fragmented sleep has shown to promote myelopoiesis and lower hypocretin release by the hypothalamus, in turn accelerating atherosclerosis progression(48).Thus perhaps WBP1L can offer insight into the intersections between sympathetic activation, neurological control, and unhealthy sleep impacting cardiovascular health, especially in women.
On a similar note of addressing sex-speci city, of relevance is FAM98A, a gene identi ed in female-speci c CPMA for interaction with long sleep.FAM98A, harboring multiple arginine demethylation sites, is a substrate of PRMT1 -an enzyme which catalyzes the synthesis of asymmetric dimethylarginine (ADMA), a molecule associated with cardiovascular harm as it induces endothelial dysfunction (49).Thus seeking to lower harmful ADMA levels to counter harmful effects of sleep loss may be relevant in preservation of vascular integrity (50).FAM98A, encoding a microtubule-associated protein, is also functionally linked to osteoclast formation, which is key to bone resorption and involved in postmenopausal osteoporosis etiology (51).Given that osteoporosis and CVD share pathology, the FAM98A locus may shed light on the importance of considering holistic treatment for hypertensive women approaching or after menopausean example being Felodipine, an antihypertensive found to additionally discourage osteoclast differentiation (52, 53) .
Apart from FAM98A, speci c genes highlight pathway connections to offer possible avenues for enhancing treatment e cacy for hypertension.Addressing the role of in ammation, SLA may lend promise as an immunosuppressant, with cytoplasm-speci c delivery of speci c domains of SLA shown to inhibit the T cell receptor functional cascade (54).ALG10B closely interacts with KCNH2 to protect it from inhibition by pharmaceuticals and thus prevent acquired long QT syndrome -interesting, as past work has identi ed KCNH2 genetic variation to associate with e cacy of speci c antihypertensive drugs (55,56).PAK5 is the effector protein of CDC42, vital for endothelial integrity and involved in the mechanism of Nebivolol, a third generation beta-blocker (57,58).CRBN, due to its intrinsic role in ubiquitination, is recruited as an E3 ligase ligand in protease-targeted chimeras (PROTACs), which hold promise in cardiovascular therapeutics -an example being P22A shown to reduce collateral damage of HMGCR upregulation caused by statins (59).These ndings point to the need for future preclinical and clinical studies to con rm the hypothesized mechanisms and test promising interventions.
Our druggability analysis speci ed genes acting as existing pharmacological targets of FDA-approved drugs, offering perspective for drug repurposing.HTR1F and KCNJ3 are linked to the serotonergic pathway and are targets of approved ADHD and antiarrhythmic drugs Atomoxetine and Dronedarone, respectively.This is potentially relevant given that serotonin may impact blood pressure regulation, and serotonin receptor desensitization is implicated in chronic sleep restriction(60, 61).HTR1F encodes for 5-HT 1F , shown to function in smooth muscle and trigeminal nerves, with its selective agonists (i.e. Lasmiditan) offering greater e cacy for migraine treatment without the collateral harm of vasoconstrictive effects induced by non-selective triptans(62).
Noticeably all 22 gene-sleep duration interaction loci we identi ed were speci c to a particular population group, a subset of population groups, or a particular sex.This may be due to substantial heterogeneity in BP architecture and sleep lifestyle as a result of cultural differences, uniquely varying stressors due to socioeconomics, and genetic risk that are both shaped by and in uence lifestyle choices.For example, admixed African and Hispanic populations are more likely to have poorly controlled hypertension and circadian abnormalities in BP regulation, as well as higher prevalence of both short and long sleep duration relative to individuals of European ancestry(63, 64).Females generally sleep longer, have higher prevalence of insomnia, and experience an increased proin ammatory response to sleep deprivation compared to males(65).Such differential risk pro les are likely attributed to a myriad of social or environmental variables along with genetic and epigenetic susceptibility(8).Therefore, it is likely that the same duration of self-reported sleep has different etiologies and physiological effects across sex and population background.Future research incorporating extensive phenotyping may help clarify whether gender-speci c or population-speci c ndings are explained by differences in sleep-related or other lifestyle behaviors, mechanisms underlying response to sleep disturbance, or are spurious.
This study has several strengths including its large-scale nature made possible by inclusion of several international biobanks and cohort studies, rigorous data harmonization and quality control protocols, and robust statistical analysis pipelines.Our ndings are reinforced by multiple lines of evidence from bioinformatics analysis.Focused druggability analysis and interpretation of drug-gene interactions offer promising insight in drug repurposing and candidate targets for future pursuits.
Limitations of this study include the risk of unidenti ed misclassi cation of self-reported sleep duration (opposed to objective measurements from actigraphy or polysomnography) due to recall bias, sleep misperception, or other psychosocial factors.Sleep health is complex, with key dimensions beyond duration (e.g., timing, quality, satisfaction, and regularity)(66).Abnormality in these other sleep dimensions were not tested here due to lack of readily available data.Adding to the complexity, sleep duration itself re ects heterogeneous health effects in uenced by genetic determinants.For example, genetic variation conducive to naturally short sleepers may even lend neuroprotection against harmful brain pathology(67).In addition, there may be residual confounding bias due to unadjusted comorbidities or environmental factors.Lastly, despite notable diversity of our sample, our data was dominated by individuals of European ancestry.It is striking that several of our loci are HIS-speci c -which may be resultant of complex admixture present in this population group.Although we were able to delve into sex-speci c interpretations for FAM98A, and WBP1L -future investigation is desired to understand the reasons behind heterogeneous effects by sex.Enrichment of sample sizes in minority populations is critical for future investigations.
In conclusion this study advances our understanding of the interaction between sleep duration extremes and genetic risk factors shaping the genetic landscape of blood pressure.Our novel discovery of 22 gene-sleep duration interaction loci both accentuates the relevance of proper sleep duration in cardiovascular health and the need to be conscious of heterogeneity present in speci c sex or population groups, providing valuable perspective for therapeutic intervention strategies to address cardiovascular disease burden.

Figures
Figure 1

Table 1
Novel Gene-Sleep Duration Interaction Loci Identi ed in Cross-Population Meta-Analysis All results herein are from the M1 model.Supplementary TableS2statistics according to each population group identi ed in cross-population results.For the sex column, C denotes combined sex, and F denotes female sex-For the alleles column, E denotes effect allele, and O denotes other allele used as reference.Bold denotes signi cance.

Table 2 .
Novel Gene-Sleep Duration Interaction Loci Identi ed Speci c to Certain Population Groups.All results herein are from the M1 model.Supplementary TableS22provides summary statistics according to each population group-speci c cohort identi ed in these population-group speci c results.For the sex column, C denotes combined sex meta-analysis, and M denotes male sex-strati ed metaanalysis.For the alleles column, E denotes effect allele, and O denotes other allele used as reference.Bold denotes signi cance.

Table 3
Novel BP Loci Identi ed by the 2df Joint Test, Not Driven by the Interaction Effect